Introduction
The retail industry is one of the most competitive industries in the world and it has always been at the forefront of technology, with data governance recently taking up a prominent role. According to the Retail Technology: Trends & IT Market Size Report
Technology and retail go hand-in-hand. Today, the retail industry is one of the most technologically savvy industries around. A customer might shop in a brick-and-mortar retail store, but she could also shop online and use technology to compare prices of products between the two channels. Social media has also become a place where people find, compare, rate, and even buy products. Customer loyalty is harder than ever to win and loyalty programs, which itemize every customer purchase, are exploding. Every brand needs a loyalty program these days. A customer can buy a product from a store in a mall and get loyalty points from the credit card used to make the purchase, the store where the purchase was made, and even from the mall where the store was located. Mall loyalty programs are a thing.
Retail customers are some of the most sophisticated customers around. They use online-offline tactics to ensure they get the best price. They want loyalty programs to understand who they are, whether they come in via mobile phone, home or work PC.
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Historical Context
The rise of physical marketplaces in cities facilitated trade, leading to the need for better inventory management systems. The 18th Century Industrial Revolution saw the mass production of goods, leading to increased availability of these goods and often at lower prices. The network of railroads springing up during that time enabled a wider distribution of products, expanding retail markets exponentially.
The early 20th Century saw the emergence of department stores, which radically changed personal shopping habits. The invention of the cash register in the late 19th century streamlined transactions. In the 70s, the introduction of barcodes revolutionized inventory data management processes and checkout procedures. In the late 20th Century, advanced POS systems emerged, helping to integrate sales data with inventory and customer management data. The advent of the internet in the 1990s paved the way for online shopping. The ecommerce revolution took off, with companies like Amazon leading the way, and, in the process, becoming a multi-billion-dollar company.
21st Century
In the 21st Century, smartphones have become mobile shopping platforms and payment gateways, putting convenience front and center. Omni-channel retailing offers an online-offline shopping experience. Today, AI is all the rage in retail, with companies using it for personalization marketing, inventory management, and customer service. One thing all of these technologies have in common is data. They have all increased the amount of data a retailer must pay to collect, manage, model, archive, and, ultimately, destroy.
In the early 2000s, retail organizations also recognized the value of data as a strategic asset. Data governance frameworks started to emerge during this time. Retailers realized they needed to implement policies and procedures to manage their data quality, data privacy, and data compliance.
In the mid-to-late 2000s, the Data Management Association (DAMA) developed frameworks and best practices for data governance, helping to formalize the discipline. Today, data governance plays a critical role in managing data quality and ensuring compliance. It plays a big role in reducing data breaches while maximizing the value of data as a strategic asset.
What is Data Governance?
In my article, Foundations of Enterprise Data Governance , I state, “Data governance is the planning, oversight, and control over management of data and the use of data and data-related resources, and the development and implementation of policies and decision rights over the use of data. It is the foundational component of an enterprise data management or enterprise information management program.”
For retailers, data governance refers to the framework and practices that ensure the effective management, quality, security, and accessibility of data throughout an organization. It defines policies, standards, and roles related to data management to support business objectives and regulatory compliance.
Data Quality Impact on Business
$12.9M
Average Annual Cost of Poor Data Quality
Organizations face significant financial consequences from poor data quality, with average annual costs reaching $12.9 million.
Source: Gartner Research, 2020
Retailers with strong data governance will have accurate, complete, and reliable data. Data stewards and data custodians will oversee robust data policies and standards, ensuring the proper usage, management, and sharing of data. They will implement policies that reveal poor data quality. They set standards for data formatting and definitions across the organization as well as control the company’s data dictionary. This serves as a reference guide for understanding the structure, relationships, and definitions of data used within an organization.
Regulatory Compliance
Regulatory compliance is a big part of data governance. Key regulations like General Data Protection Regulation (GDPR), Basel III, and the California Consumer Privacy Act (CCPA) aim to enhance individuals’ rights regarding the collection, use, and disclosure of personal data. Companies must follow these rules or run the risk of being heavily fined by the regulatory bodies. Robust data governance increases operational efficiency, while reducing the risk of data ending up in the wrong hands. Robust data governance also helps ensure data security as well as safeguards customer data and privacy.
The ultimate goal of data governance is to create a single, consistent view of key business entities (e.g., customers, products, services) from inconsistent data formats to enhance data accuracy and reduce data redundancy. Of course, this is easier said than done, but it is a worthy goal because businesses working with well-governed data will always make better business and market decisions.
The Role of Data Governance in Retail
The primary goal of data governance in retail is to ensure the accuracy, consistency, security, and accessibility of data , making the data available to the right stakeholders at the right time, thereby promoting informed decision-making and driving business value. High-quality retail data is essential for effective inventory management, customer insights, and sales forecasting. This enables timely decision-making and enhances operational efficiency.
Strong data governance will make it easier for retailers to have data teams attain that coveted single customer view, the comprehensive and unified representation of a customer’s interactions, behaviors, and data across all retail touchpoints and channels within an organization. It consolidates information from various corporate sources to provide a holistic understanding of each customer, enabling personalized marketing, improved customer service, and better decision-making.
According to The University of Edinburgh , “Data governance is the system of decision rights and accountabilities for managing the quality, availability, documentation and security of an organisation’s data assets.” In essence, a solid data governance framework is crucial for enterprise organizations because of the complexity and distribution of their data assets. It ensures the quality, security, and accessibility of data, enabling organizations to make informed decisions while maintaining a competitive business edge.
Katrina Dalao goes one step further. In her article, Data governance in retail: Protecting privacy while driving profits, Why governance is critical in an industry where the customer (data) comes first
Leveraging Big Data Analytics
In the early 2000s, Walmart, one of the largest retailers in the world, recognized technology would have an outsized role in the retail industry and embraced big data technology, optimizing its supply chain management, inventory, and pricing strategies. In 2017, Walmart developed its Data Café, a massive data warehouse that centralized the vast amounts of data coming in from Walmart’s approximately 10,500 worldwide stores. The data discovery system also streamlined the company’s data analysis processes.
The integration of big data analytics and business intelligence at Walmart provides deep insights into various aspects of the company’s operation, including with customer behavior, inventory management, and supply chain data. Today, it uses data from its point-of-sale systems to track customer behavior as well as improve decision-making and increases efficiency.
The integration of big data analytics and business intelligence at Walmart allows the company to gain deep insights into various aspects of its operations, such as customer behavior, inventory management, and even supply chain efficiency and logistics. Today, it uses data from its point-of-sale systems to track customer behavior as well as improve decision-making.
Predictive analytics enables Walmart to forecast demand more accurately, which helps reduce stockouts and excess inventory. Dynamic pricing adjusts prices in real-time based on market and even, potentially, weather conditions, maximizing sales and profitability. Prescriptive analytics then recommends a future course of action for Walmart on multiple fronts.
In their article, Strategies, Challenges, and Outcomes of Big Data Analytics for Enhanced Business Intelligence
Streamlining Operations
All of these analytical models require strong data governance. The company’s vast operations generate data silos that can hinder comprehensive analysis. “Real-time analytics need deeper integration into decision-making processes, and improving data governance is critical to ensure data quality, security, and compliance for accurate analysis and informed decisions. Addressing these key challenges now is crucial for Walmart to fully realize the potential of its big data analytics and business intelligence initiatives,” says Miller, Khan, and Johnson .
According to ProjectPro’s “How Big Data Analysis helped increase Walmarts Sales turnover?” , Walmart’s data governance framework included:
Data Quality Management: Regular data cleansing and validation processes ensured data accuracy.
Master Data Management (MDM): The creation of a single source of truth for critical data entities like products, suppliers, and customers.
Data Security Measures: The implementation of a robust data security measure to protect sensitive data.
Data Analytics Revenue Impact
$1B+
Walmart’s Incremental Revenue from Data Analysis
Walmart achieved a 10% to 15% increase in online sales, resulting in $1 billion in incremental revenue through effective implementation of big data analysis strategies.
Source: ProjectPro
The data governance initiatives resulted in Walmart improving their data quality, streamlining their operations, and enhancing their customers’ experiences through personalized marketing, which lead to a verifiable increase in customer satisfaction and loyalty.
“Walmart observed a significant 10% to 15% increase in online sales for $1 billion in incremental revenue. Big data analysts were able to identify the value of the changes Walmart made by analysing the sales before and after big data analytics were leveraged to change the retail giant’s e-commerce strategy,” says ProjectPro. Arguable, Walmart’s early adoption of big data and data governance technologies was a key factor in its ability to maintain its position as a global retail leader. However, its competitors have noticed. Walmart’s focus on data-driven decision-making has influenced the broader retail industry as well, who are all jumping on the data governance and data analytics bandwagon now.
Reducing Bias
By establishing uniform data standards and definitions across a retailer’s various systems and departments, organizations can ensure everyone is using the same data for analysis and reporting. This increases the chance that all analysts build models from the same set of data, increasing consistency in results. Using a common dataset ensures that all models are evaluated under the same conditions, making it easier to compare their performance and validity. Shared datasets promote standardization in methodologies, allowing practitioners to follow best practices and align on definitions, metrics, and modeling techniques. Collaboration is smoother when teams draw from the same data sets because communication is encouraged. The sharing of insights usually leads to better collective outcomes as well.
Implementation Essentials
A common dataset helps minimize biases that may arise from different data sources or collection methods, leading to more trustworthy and generalizable models. Data quality is assured. The chances of errors or inconsistencies across models are reduced while the efficiency of a model is maximized. Working from the same dataset streamlines the data preparation process, saving time and resources that might otherwise be spent on duplicated efforts. Models built on the same data are more easily reproduced and validated by other researchers or teams, enhancing any finding’s credibility. When multiple models are built from the same dataset, it becomes easier to learn from each modeler’s approach, which fosters innovation and improvement in modeling techniques.
Fraud Prevention
Cybercriminals have taken particular aim at retail companies because these companies have so much consumer information in their data centers. From personal details, like credit card information to geolocation data to lifestyle data to social media accounts and psychographic data that can reveal what influences a purchasing decision, retailers have a plethora of almost priceless data on their customers.
Unfortunately, the rising tide of cyberattacks on retailers won’t diminish anytime soon. With everything from malware to ransomware to malvertising to distributed denial-of-service attacks as well as a whole host of new attacks percolating in the minds of cybercriminals the world over, retailers must strengthen their cybersecurity defenses today.
As I state in my article, Data Governance in Banking: Key Insights and Best Practices
Data Breach Impact Analysis
$4.88M
Average Global Cost of Data Breach
Data breach costs hit record $4.88M in 2024, up 10% from 2023.
1 in 3
Breaches Involving Shadow Data
Shadow data now accounts for 33% of breaches, showing growing data protection challenges.
$2.22M
Cost Savings with AI Security
AI/automation in security saves organizations $2.22M on average versus non-adopters.
Strong data governance is the foundation of strong cybersecurity.
Best Practices
Data governance is a all-inclusive framework designed to effectively manage a retailer’s data assets. It encompasses several key components that ensure data integrity, security, and accessibility, which are critical for retailers, who must keep up with highly sophisticated omnichannel customers.
Develop a Data Governance Framework
Benefits of investing in data governance In his article, The Path to Modern Data Governance
Develop a Data Governance Framework Roadmap
In his article , Wells introduces a data governance framework roadmap along with several guidelines to help create a modern data governance roadmap (see Figure 1). The framework has six layers — goals, methods, people, processes, technology, and culture. Each layer handles one part of a data governance modernization initiative.
Figure 1: Data governance framework. Source: Eckerson For Wells , the framework breaks down as such:
Goals: Why is the data governed? Goals force businesses to focus on the value delivered as well as helping a business align its objectives.
Methods: How is the data governed? This includes the data policies used to govern the data as well as any guardrails set up to protect it.
People: Who governs the data? This is the most important element in Wells’s data governance framework. We don’t govern data, we govern the people using the data, Wells point out . This section includes sponsors, owners, stewards, curators, coaches, consumers, and data stakeholders, which can, admittedly, be quite a broad term that includes internal auditors and risk managers.
Processes: What are the series of actions taken to achieve a specific set of results? These could include resolving issues, coordinating changes, assuring data quality, cleaning data, cataloging datasets, as well as measuring and monitoring the data governance impact, claims Wells.
Technology: The features and functions that fill many roles in data management, including data ingestion, data cataloging, data preparation, data analysis, and data pipeline management.
Culture: The data governance environment and the establishment of norms to create an environment where data governance, agile projects, and self-service data analysis and reporting all work in harmony, says Wells.
Stay ahead with practical insights and expert perspectives on modern data governance.
A Living Document
Data governance involves monitoring and measuring data usage to identify improvements. It ensures that only the right people have the right access to the right data, even if the data is highly sensitive. Implementing a robust and reliable data governance framework allows retailers to conduct regular audits and monitor compliance. This enhances overall operational efficiency. Data governance frameworks enable effective risk management by identifying and mitigating data-related risks, which allow retailers to effectively manage credit, operational, and compliance risks.
Figure 2: Wells’ data governance framework roadmap. Source: Eckerson . Wells believes his data governance roadmap should be a living document that changes as needed. Figure 2 shows the roadmap plotted as a year-quarter-month timeline. Wells recommends revisiting and/or revised it once a quarter. Modernization, however, isn’t quick or easy, warns Wells. “It is a journey, not an event” and planning is crucial, he concludes.
Conclusion
The retail industry’s embrace of data governance has become a cornerstone of its evolution in the 21st century. As one of the most technologically advanced industries around, the retail industry has leveraged data to enhance customer experiences, drive profitability, and, above all else, optimize operations. From Walmart’s pioneering use of big data in the early 2000s to the development of its Data Café, the industry has demonstrated how data-driven decision-making can transform business outcomes.
By implementing effective data governance practices, retailers can enhance data quality, ensure compliance, improve decision-making, and ultimately drive business value. This framework of data literacy is crucial in a data-driven landscape. Accurate insights can significantly impact customer experiences and operational success.
The integration of technologies like AI, machine learning, and predictive analytics lets retailers gain deeper insights into customer behavior. It can also streamline supply chains and introduce dynamic pricing strategies into selling. However, these advancements come with challenges. Robust data governance policies are needed to ensure data quality, security, and compliance with regulations like GDPR and CCPA.
The retail industry’s future lies in its ability to harness the power of data while maintaining customer trust. As Katrina Dalao aptly noted, data governance not only protects privacy but also drives profits. It is an indispensable tool for retailers working in the digital age. By adopting best practices and continuously refining their retail data governance strategies, retailers can ensure they remain at the forefront of innovation, delivering value to both their businesses and their customers.
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